PhageTAILor leverages machine learning for phage tail-like elements detection and classification in plant-associated bacteria
- 1DOE Joint Genome Institute
Abstract
Phage tail-like elements (PTEs) -- tailocins, bacterial type VI secretion systems (T6SS), and extracellular contractile injection systems (eCIS) -- are contractile nanomachines that bacteria use to kill their neighbors and compete within their micro-ecosystems. PTEs help shape microbial community composition. Most PTE detection tools only detect a single PTE class. Moreover, most tailocin detection methods are largely restricted to Pseudomonas, leaving a key part of tailocin diversity uncharacterized. In this work, we present PhageTAILor (https://github.com/hjcho-bio/PhageTAILor), an integrative and fully automated pipeline that detects and classifies prophages and 3 PTE classes from bacterial genomes. PhageTAILor combines a 6-detector homology-based candidate search (geNomad, tail-gene, PHROGs-tail, SecReT6, eCIStem, and a divergence-tolerant tail-HMM detector) with a LightGBM classifier comprising 1 multiclass and 3 binary heads, trained on 6,501 bacterial genomes carrying 13,082 prophages and PTEs. A phylogeny-free feature matrix used in our model keeps predictions reproducible between model construction and user inference. PhageTAILor performs strongly at the genome level and generalizes beyond its Pseudomonas-rich training set. On a 76-strain cross-clade benchmark, PhageTAILor detected tailocins at F1 = 0.955. Furthermore, it identified 12 of 13 experimentally validated tailocins spanning five genera versus 2 of 13 for a Pseudomonas-restricted tool TattleTail. PhageTAILor also demonstrated sensitivity equivalent to viral detection tool geNomad while avoiding its higher false-positive rate. Applied to 7,925 plant- and soil-associated bacterial isolates, PhageTAILor showed that prophages in the phyllosphere and tailocins in plant-associated bacteria, whereas eCIS are enriched in soil. PhageTAILor is distributed as an open-source, modular pipeline with a command-line interface.
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BibTeX
@misc{cho2026phagetailor,
title = {{PhageTAILor leverages machine learning for phage tail-like elements detection and classification in plant-associated bacteria}},
author = {H. Cho and S. Hour and S. Roux and C. Coclet and O. Amusat and V. K. Mutalik and A. E. Kazakov and A. Levy and N. Nachmias and L. Aureli and T. S. Sweet and A. Visel and R. M. Ceballos and J. T. R. Basso},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.24.746745},
url = {https://www.biorxiv.org/content/10.64898/2026.08.24.746745v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - PhageTAILor leverages machine learning for phage tail-like elements detection and classification in plant-associated bacteria
AU - H. Cho
AU - S. Hour
AU - S. Roux
AU - C. Coclet
AU - O. Amusat
AU - V. K. Mutalik
AU - A. E. Kazakov
AU - A. Levy
AU - N. Nachmias
AU - L. Aureli
AU - T. S. Sweet
AU - A. Visel
AU - R. M. Ceballos
AU - J. T. R. Basso
PY - 2026
DA - 2026/08/24
PB - bioRxiv
DO - 10.64898/2026.08.24.746745
UR - https://www.biorxiv.org/content/10.64898/2026.08.24.746745v1
AB - Phage tail-like elements (PTEs) -- tailocins, bacterial type VI secretion systems (T6SS), and extracellular contractile injection systems (eCIS) -- are contractile nanomachines that bacteria use to kill their neighbors and compete within their micro-ecosystems. PTEs help shape microbial community composition. Most PTE detection tools only detect a single PTE class. Moreover, most tailocin detection methods are largely restricted to Pseudomonas, leaving a key part of tailocin diversity uncharacterized. In this work, we present PhageTAILor (https://github.com/hjcho-bio/PhageTAILor), an integrative and fully automated pipeline that detects and classifies prophages and 3 PTE classes from bacterial genomes. PhageTAILor combines a 6-detector homology-based candidate search (geNomad, tail-gene, PHROGs-tail, SecReT6, eCIStem, and a divergence-tolerant tail-HMM detector) with a LightGBM classifier comprising 1 multiclass and 3 binary heads, trained on 6,501 bacterial genomes carrying 13,082 prophages and PTEs. A phylogeny-free feature matrix used in our model keeps predictions reproducible between model construction and user inference. PhageTAILor performs strongly at the genome level and generalizes beyond its Pseudomonas-rich training set. On a 76-strain cross-clade benchmark, PhageTAILor detected tailocins at F1 = 0.955. Furthermore, it identified 12 of 13 experimentally validated tailocins spanning five genera versus 2 of 13 for a Pseudomonas-restricted tool TattleTail. PhageTAILor also demonstrated sensitivity equivalent to viral detection tool geNomad while avoiding its higher false-positive rate. Applied to 7,925 plant- and soil-associated bacterial isolates, PhageTAILor showed that prophages in the phyllosphere and tailocins in plant-associated bacteria, whereas eCIS are enriched in soil. PhageTAILor is distributed as an open-source, modular pipeline with a command-line interface.
ER -